Inertial sensors are used in many navigation systems to provide data to compute position, acceleration, heading, etc. Inertial sensors are subject to various sources of error. For example, two common errors are bias and scale factor. Bias and scale factor errors each have two components, a run-to-run (also referred to as repeatability) and an in-run (also referred to as stability) component. The repeatability component typically changes each time the system is powered on and off, but remains constant when the system is on. The stability component varies while the system is powered on.
Typical auto-calibration techniques do not provide an optimal implementation for some navigation systems, such as MEMS-based navigation systems. In some systems, such as MEMS, the stability component of the error may be the same or larger than the repeatability component. In these systems, when typical auto calibration techniques are used, the sensor error estimate that is stored in memory for use the next time the system is turned on will essentially be a random value due to the large size of the stability component of the sensor error. This makes the typical auto-calibration approach ineffective for some navigation systems.